So far, the problem caused by OOD data has been investigated only on toy data sets, e.g. a model trained on the CIFAR-10 data set [1]}, learning to classify automobiles and trucks, is tested on the SVHN data set [2]} containing house numbers.
This raises the question if the test performance of proposed OOD detectors translate to an existing model trained on chest X-rays.
Figure REF  motivates this problem: as the real world data consists of more then frontal chest X-rays a classifier like CheXnet [3]} must handle OOD images safely.
<FIGURE>